Crossover recombination-based global-best brain storm optimization algorithm for UAV path planning

Path planning of the UAV is one of the complex optimization problems, due to the model complexity and a high number of constraints. In addition, the flyability of path is also a requirement for 3D UAV path planning in practical environment. Evolutionary algorithms are effective solutions to solve complex optimization problems with multiple constraints. Regarding the local adjustment characteristic of cubic B-spline curves and crossover recombination in differential evolution algorithm, we design and implement a crossover recombination based global-best brain storm optimization (GBSO) algorithm to solve multi-constraints 3D path planning problem with considering the continuous curvature of path. The cost function is formulated includes the safety, economy and flyability, where the characteristic polygon vertices of a cubic B-spline curve representing the path are taken as the optimization variables. Simulation results and comparison analysis demonstrate that the proposed method has a better performance than GBSO, SHADE and other compared algorithms for UAV path planning.<p></p>